다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification

https://ir.cwi.nl/pub/30568/1-s2.0-S096014811831231X-main.pdf

Machine learning methods for wind turbine condition monitoring: A review

https://hal.archives-ouvertes.fr/hal-01714496/document

The infogram: Entropic evidence of the signature of repetitive transients

Wavelet transform based on inner product in fault diagnosis of rotating machinery: A review

Multiwavelet transform and its applications in mechanical fault diagnosis – A review

Wind turbine fault detection using multiwavelet denoising with the data-driven block threshold

Planetary gearboxes exhibit complicated dynamic responses which are more difficult to detect in vibration signals than fixed-axis gear trains because of the special gear transmission structures. Diverse advanced methods have been developed for this challenging task to reduce or avoid unscheduled breakdown and catastrophic accidents. It is feasible to make fault features distinct by using multiwavelet denoising which depends on the feature separation and the threshold denoising. However, standard and fixed multiwavelets are not suitable for accurate fault feature detections because they are usually independent of the measured signals. To overcome this drawback, a method to construct customized multiwavelets based on the redundant symmetric lifting scheme is proposed in this paper. A novel indicator which combines kurtosis and entropy is applied to select the optimal multiwavelets, because kurtosis is sensitive to sharp impulses and entropy is effective for periodic impulses. The improved neighboring coefficients method is introduced into multiwavelet denoising. The vibration signals of a planetary gearbox from a satellite communication antenna on a measurement ship are captured under various motor speeds. The results show the proposed method could accurately detect the incipient pitting faults on two neighboring teeth in the planetary gearbox.

https://www.mdpi.com/1424-8220/13/1/1183/pdf

Customized Multiwavelets for Planetary Gearbox Fault Detection Based on Vibration Sensor Signals

Wavelet denoising is an effective approach to extract fault features from strong background noise It has been widely used in mechanical fault detection and shown excellent performance However, traditional thresholds are not suitable for nonstationary signal denoising because they set universal thresholds for different wavelet coefficients Therefore, a data-driven threshold strategy is proposed in this paper First, the signal is decomposed into different subbands by wavelet transformation Then a data-driven threshold is derived by estimating the noise power spectral density in different subbands Since the data-driven threshold is dependent on the noise estimation and adapted to data, it is more robust and accurate for denoising than traditional thresholds Meanwhile, sliding window method is adopted to set a flexible local threshold When this method was applied to simulation signal and an inner race fault diagnostic case of dedusting fan bearing, the proposed method has good result and provides valuable advantages over traditional methods in the fault detection of rotating machines

Hailiang Sun

Papers

Multiwavelet transform and its applications in mechanical fault diagnosis – A review

Wind turbine fault detection using multiwavelet denoising with the data-driven block threshold

Customized Multiwavelets for Planetary Gearbox Fault Detection Based on Vibration Sensor Signals

A data-driven threshold for wavelet sliding window denoising in mechanical fault detection

Customized maximal-overlap multiwavelet denoising with data-driven group threshold for condition monitoring of rolling mill drivetrain